
Show Notes
Ben Poland walks through Faire's complete CI transformation, from a single Jenkins instance struggling with thousands of lines of Groovy to a distributed Buildkite system running across multiple Kubernetes clusters. He details the technical challenges of running CI workloads at scale, including API rate limiting, etcd pressure points, and the trade-offs of splitting monolithic pipelines into service-scoped ones. You will learn: How to architect CI systems that match team ownership and eliminate shared failure points across services
Kubernetes scaling patterns for CI workloads, including multi-cluster strategies, predictive node provisioning, and handling API throttling
Performance optimization techniques like Git mirroring, node-level caching, and spot instance management for variable CI demands
Migration strategies and lessons learned from moving away from monolithic CI, including proof-of-concept approaches and avoiding the sunk cost fallacy
Sponsor This episode is brought to you by Testkube—where teams run millions of performance tests in real Kubernetes infrastructure. From air-gapped environments to massive scale deployments, orchestrate every testing tool in one platform. Check it out at testkube.io More info Find all the links and info for this episode here: https://ku.bz/klBmzMY5-
Interested in sponsoring an episode? Learn more.
More from KubeFM
Non-deterministic agents pose specific challenges for platform teams in observability, state management, governance, and trust. Mauricio (Salaboy) Salatino explains why agentic applications behave like distributed multi-agent systems. His test assigned agents to take an order, cook the pizza, deliver it, and charge the customer. One order crossed 15 containers and produced 200 traces. In this interview: Why agent frameworks can recreate monolith scaling and resource contention
KubeSelect is a new show that tests one bold Kubernetes hypothesis with one expert. Hosts Salman Iqbal and Bart Farrell examine the evidence and ask whether the claim holds up. In episode two, Kat Cosgrove tackles a long-running question: should teams run databases on Kubernetes? StatefulSets, persistent volumes, CSI, and database operators changed the technical answer, but they did not remove the operational risk. In this episode: How Kubernetes storage has matured and why old assumptions persist
Kube Select takes one bold Kubernetes hypothesis and tests it with an expert. In the first episode, Salman Iqbal and Bart Farrell ask whether most organizations have an observability problem or a decision-making problem. Henrik Rexed, Senior Staff Engineer at Dynatrace, challenges the original claim. Teams can collect metrics, logs, and traces, but that telemetry needs system relationships, ownership, and deployment history to support a confident decision. In this episode: Why telemetry without context slows incident response
Kube Signals starts where the keynote ends: with the trends that platform teams will have to operationalize next. In this special episode, Brian Teller speaks with Saiyam Pathak about his KubeCon India keynote and the shift from developer platforms to AI factories. They examine what GPU scarcity, shared accelerators, and AI workloads mean after the conference slides meet real infrastructure. In this interview: Why GPU infrastructure is becoming a platform-engineering concern
At enterprise scale, a deployment pipeline that runs Helm upgrades directly against Kubernetes hides drift, mixes configuration with CI logic, and makes the last pipeline run the source of truth. Elad Cohen explains how WSC Sports moved from Azure DevOps to GitHub Actions and redesigned delivery around Git and Argo CD. The resulting platform separates builds from deployments, keeps service configuration in values files, and continuously reconciles clusters. In this interview: Why CI should change Git instead of the cluster